This article provides a review of the achievements and advancements in dental technology brought about by computer-aided design and the all powerful finite element method of analysis. The scope of the review covers dental implants, jawbone surrounding the implant and the biomechanical implant and jawbone interaction. Prevailing assumptions made in the published finite element analysis, and their limitations are discussed in some detail which helps identify the gaps in research as well as future research direction.

The slow adoption of Bridge Management Systems (BMSs) and its impractical future prediction of the condition rating of bridges are attributed to the inconsistency between BMS inputs and bridge agencies' existing data for a BMS in terms of compatibility and the enormous number of bridge datasets that include historical structural information. Among these, historical bridge element condition ratings are some of the key pieces of information required for bridge asset prioritisation but in most cases only limited data is available.This study addresses the abovementioned difficulties faced by bridge management agencies by using limited historical bridge inspection records to model time series element level data. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating historical bridge condition ratings using limited bridge inspection records. The BPM employs historical non-bridge datasets such as traffic volumes, populations and climates, to establish correlations with existing bridge condition ratings from very limited bridge inspection records. The resulting model predicts the missing historical condition ratings of individual bridge elements. The outcome of this study can contribute to reducing the uncertainty in predicting future bridge condition ratings and so improve the reliability of various BMS analysis outcomes.Keywords: Bridge Condition Ratings, Bridge Management System (BMS), Artificial Neural Network (ANN), Backward Prediction Model (BPM) 1.0Introduction The efficient use of public funds for the well-being of bridge networks requires an effective bridge asset management technology. It is particularly important to optimise future bridge maintenance, repair and rehabilitation (MR&R) activities with the given funding and to request suitable future funding based on reliable Bridge Management System (BMS) outcomes. A BMS, as a computer-based decision support system (DSS), is used to determine the best possible strategy that ensures an adequate level of safety for bridges at the lowest possible life-cycle cost [1]. Many bridge agencies worldwide have begun the transition to BMS-based bridge asset management. A BMS, based on the results of a deterioration model, provides various important future estimations for the planning of MR&R activities. The success of a BMS is highly 2 dependent on the accurate estimation of future condition ratings [2]. The condition ratings are used directly and indirectly as input data for many significant functions in the commercial BMS package [3]. Fig. 1 presents the uses of bridge condition ratings and the relationship with many analytical BMS modules in project and network level analysis. Ideally, a BMS should identify current and future bridge deficiencies and estimate the backlog of funding requirements. Typical BMS software mainly functions to [3,4]: (1) forecast future bridge deficiencies; (2) identify a list of improvement options to correct such deficiencies; and (3) estimate the costs...

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Objectives: Using the finite element technique, the stress characteristics within the mandible are evaluated during a dynamic simulation of the implant insertion process. Implantation scenarios considered are implant thread forming (S1), cutting (S2) and the combination of forming and cutting (S3). Ultimately, the outcome of this study will provide an improved understanding of the failure mechanism consequential to the stress distribution characteristics in the mandible during the implantation process. Material and Methods: Parameters considered herein include bone cavity diameters of 3.9mm (for S2), 4.25mm (for S1) and a tapered cavity of diameters linearly varying from 3.9 to 4.25mm (for S3). The bone-implant system is modelled using three-dimensional tetrahedral elements. Idealised bone and implant interaction properties are assumed. The stress profiles in the mandible are examined for all bone cavity diameters. Results and Conclusion: The stress levels within the cancellous and cortical bone for S1 are significantly reduced when compared to scenarios S2 and S3. For S3, during the initial insertion steps, the stress is marginally less than that for S2. Close to the end of the insertion process, the stress level within the cancellous bone in S3 is approximately half way between that of S1 and S2. Generally for all scenarios, as the insertion depth increases the stress increases less significantly in the cortical bone than in the cancellous bone. Overall, different implant surface contact areas are the major contributors to the different stress characteristics of each scenario.

Through the combination of both dental and engineering expertise, a simplified and efficient modelling technique is developed. This improves the understanding of the biomechanical reaction that the jawbone exhibits due to the insertion of implant. The current research is a pilot study using the FEM to model and simulate the dental implantation process. The assumptions made in the modelling and simulation process are: (1) the implantation process is simulated as a step-wise process instead of a continuous process; (2) the implant is parallel threaded and the implant does not rotate during insertion into the jawbone. Although the modelling and simulation techniques had to be simplified, a significant amount of information is gained that helps lay a good foundation for future research. Recommendations for future studies include the variation of the torque applied during the implantation process and upgrading the software capabilities to simulate the full dynamical process of implantation.